Biology subjects

Pechman, K. R.

Publications and source records attributed to Pechman, K. R..

3 recordsLinked to original sources

bioRxiv2023

MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal

ObjectiveData harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. MethodsWe validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. ConclusionMidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. SignificanceUsers should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.

neuroscience↗

bioRxiv2023

A convolutional neural network estimation of brain age is sensitive to cognitive impairment and decline

The greatest known risk factor for Alzheimers disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62x10-32; T1: r=0.61, p=1.45x10-26, FW+T1: r=0.77, p=6.48x10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: {beta}=-1.094, p=6.32x10-7; T1: {beta}=-1.331, p=6.52x10-7; FW+T1: {beta}=-1.476, p=2.53x10-10; executive function, FW: {beta}=-1.276, p=1.46x10-9; T1: {beta}=-1.337, p=2.52x10-7; FW+T1: {beta}=-1.850, p=3.85x10-17) and longitudinal cognition (memory, FW: {beta}=-0.091, p=4.62x10-11; T1: {beta}=-0.097, p=1.40x10-8; FW+T1: {beta}=-0.101, p=1.35x10-11; executive function, FW: {beta}=-0.125, p=1.20x10-10; T1: {beta}=-0.163, p=4.25x10-12; FW+T1: {beta}=-0.158, p=1.65x10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.

neuroscience↗

bioRxiv2022

Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1,184 participants

Quantifying the microstructural and macrostructural geometrical features of the human brains connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal datasets totaling in 1184 subjects and 2236 sessions of people aged 50-97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging dataset, Cambridge Centre for Ageing Neuroscience dataset, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could have widespread implications for a variety of neurodegenerative disorders.

neuroscience↗